SCV Filter: A Hybrid Deep Learning Model for SARS-CoV-2 Variants Classification


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Аннотация

Background:The high mutability of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) makes it easy for mutations to occur during transmission. As the epidemic continues to develop, several mutated strains have been produced. Researchers worldwide are working on the effective identification of SARS-CoV-2.

Objective:In this paper, we propose a new deep learning method that can effectively identify SARSCoV-2 Variant sequences, called SCVfilter, which is a deep hybrid model with embedding, attention residual network, and long short-term memory as components.

Methods:Deep learning is effective in extracting rich features from sequence data, which has significant implications for the study of Coronavirus Disease 2019 (COVID-19), which has become prevalent in recent years. In this paper, we propose a new deep learning method that can effectively identify SARS-CoV-2 Variant sequences, called SCVfilter, which is a deep hybrid model with embedding, attention residual network, and long short-term memory as components.

Results:The accuracy of the SCVfilter is 93.833% on Dataset-I consisting of different variant strains; 90.367% on Dataset-II consisting of data collected from China, Taiwan, and Hong Kong; and 79.701% on Dataset-III consisting of data collected from six continents (Africa, Asia, Europe, North America, Oceania, and South America).

Conclusion:When using the SCV filter to process lengthy and high-homology SARS-CoV-2 data, it can automatically select features and accurately detect different variant strains of SARS-CoV-2. In addition, the SCV filter is sufficiently robust to handle the problems caused by sample imbalance and sequence incompleteness.

Other:The SCVfilter is an open-source method available at https://github.com/deconvolutionw/SCVfilter.

Авторлар туралы

Han Wang

College of Information Science and Technology, Beijing University of Chemical Technology

Email: info@benthamscience.net

Jingyang Gao

College of Information Science and Technology, Beijing University of Chemical Technology

Хат алмасуға жауапты Автор.
Email: info@benthamscience.net

Әдебиет тізімі

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